Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z

0:00Hey, everyone. Welcome to the Latent Space podcast live from A16Z. This is Alessio, Fandero Kernel Lens, and I'm joined by Twix, editor of Late in Space. Hey, hey, hey. And we're so glad to be on with you guys. Also, a top AI podcast, Martin Casado and Sarah Wang. Welcome. Very happy to be here and welcome. Yes. We love this office. We love what you've done with the place. The new logo is everywhere now. It's still getting, it takes a while to get used to, but it reminds me of, like, sort of a callback to a more ambitious age, which I think is kind of... Definitely makes a statement. Yeah. Yeah, not quite sure what that statement is, but it makes it statement.

0:37Martin, I go back with you to Netlify. Yep. And, you know, you created software-defined networking and all that stuff. People can read up on your background. Yep. Sarah, I'm newer to you. You sort of started working together on AI infrastructure stuff. That's right. Yeah, seven years ago now. Best growth investor in the entire industry. Oh, say more. Hands down. Sarah is. I mean, when it comes to AI companies, Sarah, I think, has done the most kind of aggressive investment thesis around AI models. So she worked with Noam Chazir, Mira, Ilya, Fei-Fei. And so just these frontier kind of like large AI models, I think Sarah's been the broadest investor.

1:19Is that fair? No. Well, I was going to say, I think it's been a really interesting tag team, actually, just because a lot of these big C deals, not only are they raising a lot of money, it's still a tech founder bet, which obviously is inherently early stage, but the resources... I was going to say, the resources, one, they just grow really quickly, but then two, the resources that they need day one are kind of growth scale. So the hybrid tag team that we have is quite effective, I think. What is growth these days? You know, you don't wake up if it's less than a billion or like... It's actually very like...

1:54No, it's a very interesting time in investing because I take the character around. These tend to be pre-monetization, but the dollars are large enough that you need to have a larger fund. And the analysis, because you've got lots of users because this stuff has such high demand, requires more of a number sophistication. And so most of these deals, whether it's us or other firms on these large model companies are like this hybrid between venture and growth. Yeah, totally. And I think stuff like BD, for example, you wouldn't usually need BD when you were seed stage trying to get product marketing.

2:25BizDev, exactly. place but like now i'm not familiar what what does biz dev mean for a venture fund because i know what biz dev means for a company yeah you know so a good example is i mean we talk about buying compute but there's a huge negotiation involved there in terms of okay do you get equity for the compute what what sort of partner are you looking at is there a go-to-market arm to that um and these are just things on this scale hundreds of millions you know maybe six months into the inception of a company, you just wouldn't have to negotiate these deals before. Yeah. These large rounds are very complex now.

2:58Like in the past, if you did a series A or a series B, like whatever, you're writing a 20 to a$60 million check and you call it a day. Now you normally have financial investors and strategic investors. And then the strategic portion always still goes with like these kinds of large compute contracts, which can take months to do. And so it's very different ties. I've been doing this for 10 years. I've never seen anything like this. Yeah. Do you have worries about the circular funding from some of these strategics no listen as long as the demand is there like the demand is there like the problem the internet is the demand wasn't there exactly all right this is this is like the whole pyramid scheme bubble thing where like as long as you mark to market on like the notional value of like these deals fine but like once it starts to chip away it really well no it's like as long as there's demand i mean you know this is like a lot of these sound bites have already become kind of cliches but they're worth saying it right like During the internet days, we were raising money to put fiber in the ground that wasn't used.

3:55That's a problem, right? Because now you actually have a supply overhang. And even in the time of the internet, the supply and bandwidth overhang, even as massive as it was, as massive as the crash was, only lasted about four years. But we don't have a supply overhang. There's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if someone invests in a company that, you know, they'll actually use the GPUs and on the other side of it is the customer. So I think it's a different time. I think the other piece, maybe just to add on to this, and I'm going to quote Martin in front of him, but this is probably also a unique time in that for the first time, you can actually trace dollars to outcomes, right?

4:36provided that scaling laws are holding and capabilities are actually moving forward. Because if you can translate dollars into a capability improvement, there's demand there, to Martine's point. But if that somehow breaks, obviously that's an important assumption in this whole thing to make it work. But instead of investing dollars into sales and marketing, you're investing into R &D to get to the capability increase. And that's sort of been the demand driver. Because once there's an unlock there, people are willing to pay for it. Is there any difference in how you build the portfolio now that some of your growth companies are like the infrastructure of the early stage companies?

5:13Like, you know, OpenAI is now the same size as some of the cloud providers were early on. Like, what does that look like? Like, how much information can you feed off each other between the two? There's so many lines that are being crossed right now or blurred, right? So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So what is a model company? It's clearly infrastructure, right? Because it's doing kind of core R &D. It's a horizontal platform, but it's also an app because it touches the users directly. And then, of course, the growth of these is just so high.

5:50And so I actually think you're just starting to see a new financing strategy emerge. And we've had to adapt as a result of that. And so there's been a lot of changes. You're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales in which it's happened is pretty phenomenal. And where we'd normally cut lines before is blurred a little bit. But that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out and the internet build out as well. Yeah. Yeah, I think it's interesting.

6:26I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, you raise money for compute, you pour the money into compute, you get some sort of breakthrough, you funnel the breakthrough into your vertically integrated application. That could be ChatGPT, that could be Cloud Code, whatever it is. You massively gain share and get users. Maybe you're even subsidizing at that point, depending on your strategy. You raise money at the peak momentum and then you rinse and repeat. And that wasn't true even two years ago, I think.

7:02And so it's sort of just tying it to fundraising strategy, right? There's a hiring strategy. All of these are tied. I think the lines are blurring even more today where everyone is... But of course, these companies all have API businesses. And so there are these frenemy lines that are getting blurred in that. A lot of... I mean, they have billions of dollars of API. revenue, right? And so there are customers there, but they're competing on the app layer. Yeah. So this is a really, really important point. So I would say for sure, venture and growth, that line is blurry. App and infrastructure, that line is blurry.

7:34But I don't think that changes our practice so much. But like where the very open questions are like, does this layer in the same way compute traditionally has? Like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. but that might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app. Like it necessarily goes down just because there are no abstractions. So those are kind of the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is in the past, if you raised money, then you basically had to wait for engineering to catch up, which famously doesn't scale.

8:16Like the mythical man must take a very long time, but like, that's not the case here. Like a model company can raise money and drop a model in a year and it's better. Right. And it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before. And I think everybody's trying to understand what the consequences are. So I think it's less about like big companies and growth and this and more about these more systemic questions that we actually don't have answers to yeah like at kernel labs one of our ideas is like if you had unlimited money to spend productively to turn tokens into products like the whole early stage market is very different because today you're investing x amount of capital to win a deal because of price structure and whatnot and you're kind of pot committing to a certain strategy for a certain amount of time but if you could like iteratively spin out companies and products and just throw i want to spend a million dollar of inference today and get a product out tomorrow.

9:19We should get to the point where the friction of token to product is so low that you can do this. And then you can change the early stage venture model to be much more iterative. And then every round is either 100K of inference or like 100 million from A16Z. There's not like$8 million a round anymore. But there's an industry structural question that we don't know the answer to, which involves the frontier models, which is let's take Anthropic. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that a company is building smaller models that use the bigger model in the background, and you open 4.5, but they add value on top of that.

10:05Now, if Anthropic can raise three times more every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. Imagine like a star that's just kind of expanding. So there could be a systemic situation where the SOTA models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don't think we've ever seen before, just because we were so bottlenecked on engineering. And it's a very open question. yeah it's almost like bitter lesson applied to the startup industry 100 yeah it literally becomes an issue of like raise capital turn that directly into growth use that to raise three times more and if you can keep doing that you literally can outspend any company that's built not any company you can outspend the aggregate of companies on top of you and therefore you'll necessarily take their share which is crazy would you say that kind of happens a character is that the this postmortem on what happened um no yeah because i think i mean the actual postmortem is he wanted to go back to google but like that's another difference we should actually talk about this yeah go for it take it well yeah i was gonna say i think um the the character thing raises actually a different issue which actually the frontier labs will face as well so we'll see how they handle it.

11:34But so we invest in character in January 2023, which feels like eons ago. I mean, three years ago feels like lifetimes ago. But and then they did the IP licensing deal with Google in August 2024. And so, you know, at the time, Noam, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world. That's obviously changed drastically. But he went to go do that. But he had a product attached. The goal was, I mean, it's Noam Shazir. He wanted to get to AGI. That was always his personal goal. But I think through collecting data and this sort of very human use case that the character product originally was and still is, was one of the vehicles to do that.

12:17I think the real reason that if you think about the stress that any company feels before you ultimately go on one way or the other is sort of this AGI versus product. And I think a lot of the big, I think OpenAI is feeling that. Anthropic, if they haven't felt it, certainly given the success of their products, they may start to feel that soon. And they're real, I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new research, right? Is it long-term research? Is it toward near to midterm research?

12:55And so in a case where you're resource constrained, of course there's this fundraising game you can play right but the fun the market was very different back in 2023 too I think the best researchers in the world have this dilemma of okay I want to go all in on AGI but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to AGI and so it does make you know I think it sets up an interesting dilemma for any startup that has trouble raising up until that level right and certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.

13:32I would say that because, because we're keeping track of all of the things that are different, right? Like, you know, venture growth and app infra. And one of the ones is definitely the personalities of the founders. It's just very different this time. I mean, I've been doing this for a decade and I'm bending startups for 20 years. And so, I mean, a lot of people start this to do AGI. And we've never had like a unified North star that I recall in the same way. Like people built companies to start companies in the past. Like that was what it was. Like I would create an internet company. I would create an infrastructure company.

14:05Like it's kind of more engineering builders. And this is kind of a different, you know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider AGI, but others have not. And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement, you know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know, the time of like Shockley and the Trader Joe Sade or something like that way back to the beginning of the industry. I mean, it's a very, very unusual time of personnel.

14:39Totally. And I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude. Very rarely can you see someone get poached for five billion dollars that's hard to compete with and then secondly if you're a founder in ai you could fart and it would be on the front page of you know the information these days and so there's sort of this fishbowl effect that i think adds to the deep anxiety that that these ai founders are feeling uh yes i mean just on a briefly comment on the founder uh the sort of talent wars thing i feel like 2025 was just like a blip like i don't know if we'll see that again because meta built the team like i don't know if i think i think they're kind of done and like who's going to pay more than meta i don't know i agree so it feels this way to me too it's like it was like basically zuckerberg kind of came out swinging and then now he's kind of back to building yeah yeah you know you got to like pay up to like assemble team to rush the job whatever but then now now you like you made your choices and now they got a ship right like i mean the other side of that is like you know like we're actually in the job hiring market we've got 600 people here i hire all the time i've got three open racks if anybody's interested that's listening to their investor yeah on the team like on the investing side of the team like and um a lot of the people we talk to have acting you know active um offers for 10 million a year or something like that and like you know and we pay really really well and just to see what's out on the market is really is really remarkable and so i would just say it's actually so you're right like the really flashy one like i will get someone for you know a billion dollars but like the inflated um trickles down yeah it's still very active today i mean yeah you could be an l5 and get an offer in the tens of millions yeah easily yeah so i think you're right that it felt like a blip hope i hope you're right um but i think it's been the steady state is everything i pulled up yeah exactly yeah for sure yeah yeah and i think that's breaking the early stage founder math too I think before a lot of people were like, well, maybe I should just go be a founder instead of like getting paid 800K a million at Google.

16:41But if I'm getting paid five, six million, that's different. But on the other hand, there's more strategic money than we've ever seen historically. Right. And so the economics, the calculus on the economics is very different in a number of ways. And it's causing a ton of change and confusion in the market. Some very positive, some negative. So, for example, the other side of the co-founder acquisition, Mark Zuckerberg poaching someone for a lot of money, we're actually seeing historic amount of M &A for basically aqua hires. Really good outcomes from a venture perspective that are effective aqua hires.

17:25So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way. yeah um let's talk maybe about what's not being invested in like maybe some interesting ideas that you will see more people build or it seems in a way you know as yc has gotten more popular it's like x has gotten more popular there's a startup school path that a lot of founders take and they know what's hot in the vc circles and they know what gets funded uh and there's maybe not as much risk appetite for things outside of that um i'm curious if you feel like that's true and whether or maybe some of the areas that you think are under discussed?

18:06I mean, I actually think that we've taken our eye off the ball on a lot of just traditional software companies. So I mean, I think right now there's almost a barbell. Like you're like the hot thing on X, you're a deep tech, right? But I feel like there's just kind of a long list of good companies that will be around for a long time in very large markets. say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like they have a really hard time getting, um, the attention of investors.

18:43And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just the silliest thing to say. I mean, think of yourself as like an individual person, like, like your personal money, right? So your personal money, will you put it in the stock market at 7 % or you put it in this company growing 5X in a very large market? Of course you can put in the company 5x so it's just like we say these stupid things like if you're not going from zero to 100 but like those like who knows what the margins of those are when clearly these are good investments for anybody right like our lps want whatever 3x net over you know the life cycle of a fund right so a company in a big market growing 5x is a great investment we'd everybody would be happy with these returns but we've got this kind of mania on these these strong growths and so i would say that that's probably the most under-invested sector right now boring software boring enterprise software just traditional like really good no no ai here well well yeah yeah of course is pulling them into use cases but that's not what they are they're not on the token path right let's just say that like they're software but they're not on the token path like these are like they're great investments from any definition except for like random vc on twitter saying vc on x saying like it's not growing fast enough what do you think maybe i'll answer a slightly different question but adjacent to what you asked um which is maybe an area that we're not investing right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.

20:06And it would probably be on the hardware side, actually. Right. And the robotics sector, right. Which is it's I don't want to say that it's not getting funding because it's clearly it's sort of non-consensus to almost not invest in robotics at this point. But we spent a lot of time in that space. And I think for us, we just haven't seen the ChatGPT moment happen on the hardware side. And the funding going into it feels like it's already taking that for granted. Yeah, yeah. But we also went through the drone, you know, there's a zipline right out there. Was that? Oh, yeah, there's a zipline, yeah.

20:37The drone, the AV era. One of the takeaways is when it comes to hardware, most companies will end up verticalizing. Like if you're investing in a robot company for agriculture, you're investing in an ag company because that's the competition and that's the pricing and that's the supply chain. And if you're doing it for mining, that's mining. And so the AD team does a lot of that type of stuff because they're actually set up to diligence that type of work. But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for purpose. And so we kind of like to look at software solutions or horizontal solutions, like applied intuition clearly from the AV wave, deep math clearly from the AV wave.

21:17I would say scale AI was actually a horizontal one for robotics early on. So that sort of thing, we're very, very interested. but the actual like robot interacting with the world is probably better for a different team yeah i'm curious who these teams are supposed to be that invested them i feel like everybody's like yeah robotics it's important and like people should invest in it but then when you look at like the numbers like the capital requirements early on versus like the moment of okay this is actually gonna work let's keep investing that seems really hard to predict in a way that it's not I mean, CO2, COSLA, GC.

21:54I mean, these are all invested in hardware companies. You just, you know. And listen, I mean, it could work this time for sure, right? I mean, if Elon's doing it, he's like, just the fact that Elon's doing it means that there's going to be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just because you have this North Star who's Elon with a humanoid, and that's going to, like, basically will into being an industry. but we've just historically found like we're a huge believer that this is going to happen we just don't feel like we're in a good position to diligence these things because again robotics companies tend to be vertical you really have to understand the market they're being sold into like that's like that competitive equilibrium with a human being is what's important it's not like the core tech and like we're kind of more horizontal core tech type investors and this is sarah and i the ad team is they can actually do these types of things uh just to clarify ad stands for?

22:44American Dynamism. I actually do have a related question. First of all, I want to acknowledge also just on the chip side. I recall a podcast where you were on, I think it was the ACS &Z podcast, about two or three years ago where you suddenly said something which really stuck in my head about how at some point, at some point, kind of scale, it makes sense to build a custom ASIC per run. Yes, it's crazy. I think you estimated 500 billion something. No, no, no. A billion dollar training run. A one billion dollar training run it makes sense to actually do a custom ASIC if you can do it in time. The question now is timelines but not money.

23:20Because just rough math. If it's a billion dollar training run then the inference for that model has to be over a billion otherwise it won't be solvent. So let's assume if you could save 20%, which you save much more than that with an ASIC, 20 % that's$200 million, you can tape out a chip for$200 million. So now you can literally justify economically not time line wise that's a different issue an asic per model because that's how much we leave on the table every single time we we do like generic nvidia yeah exactly exactly no it's actually much more than that you could probably get you know a factor of two which would be 500 million typical mfu would be like 50 yeah yeah and that's good exactly yeah um so so yeah i mean and i just want to acknowledge like here we are in in 2025 and opening eyes confirming like broadcom and all the other like custom silicon deals which is incredible i think that uh you know speaking about ad there's there's a really like interesting tie-in that obviously you guys are hit on which is like these sort of like america first movement or like sort of re-industrialize here and then uh move tsmc here if that's possible um how much overlap is there from ad yeah to i guess growth and uh investing in particularly like you know us ai companies that are strongly bounded by their compute yeah so i mean i would view i would view ad is more as a market segmentation than like a mission right so the market segmentation is it has kind of regulatory compliance issues or government you know sale or deals with like hardware i mean they're just set up to to to to to diligence those types of companies so it's more of a market segmentation thing i would say the entire firm you know which has been since it's been incepted you know has geographical biases right i mean for the longest time we're like you know bay area is going to be like where the majority of the dollars go yeah and and listen there's actually a lot of compounding effects for having a geographic bias right you know everybody's in the same place you've got an ecosystem you're there you've got presence you've got a network um and i mean i would say the bay area is very much back you know like i i remember during pre-covid like it was like almost crypto had kind of pulled startups away from yeah yeah new york was you know because it's so close to finance came up like los angeles had a moment because it was so close to consumer but now it's kind of come back here and so i would say you know we tend to be very bay area focused historically even though of course we invest all over the world and then i would say like if you take the ring out you know one more it's going to be the u.s of course because we know very well and then one ring more is going to be getting us and its allies and yeah and it goes from there yeah sorry no no i agree i think from a but i think from the internet that's sort of like where the companies are headquartered maybe your questions on supply chain and customer base i would say our customers are our companies are fairly international from that perspective like they're selling globally right they have global supply chains in some cases i would say also the stickiness is very different yeah historically between venture and growth like there's so much company building in venture so much so like hiring the next pm introducing the customer like all of that stuff like of course we're just going to be stronger where we have our network and we've been doing business for 20 i've been in the bay area for 25 years so clearly i'm just more effective here than i would be somewhere else um where i think i think for some of the later stage rounds the companies don't need that much help.

26:30They're already kind of pretty mature historically. So like they can kind of be everywhere. So there's kind of less of that stickiness. This is definitely in the AI time. I mean, Sarah is now the chief of staff of like half the AI companies in the Bay Area right now. She's like ops ninja, biz dev, biz ops. Are you finding much AI automation in your work? Like what is your stack? Oh, in my personal stack? I mean, because like, by the way, the reason for this is triggering. Yeah, like I'm hiring ops people. A lot of founders I know are also hiring ops people. And I'm just, you know, it's opportunity since you're also like basically helping out with ops with a lot of companies.

27:09What are people doing these days? Because it's still very manual as far as I can tell. Yeah. I think the things that we help with are pretty network based in that it's sort of like, hey, how do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that. I will say as a growth investor, Claude Cowork is pretty interesting. Like for the first time, you can actually get one-shot data analysis, right? Which, you know, if you're going to do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before.

27:40And our team, the other, it was like midnight and the three of us were playing with Claude Cowork. We gave it a raw file. Boom. Perfectly accurate. we checked the numbers it was amazing that was my like aha moment that sounds so boring but you know that's that's the kind of thing that a growth investor is like you know slaving away on late at night um done in a few seconds yeah you gotta wonder what the whole like anthropic labs which is like their new sort of products studio what would that be worth as an independent uh startup you know like a lot yeah true yeah you gotta hand it to them they've been executing incredibly well yeah i mean to me like you know anthropic like building on cloud code i think it makes sense to me the the real um pedal to the metal whatever the the phrase is is when they start coming after consumer with against open ai and like that is like red alert at open oh i think they've been pretty clear they're enterprise focused they have been but like here's like publicly it's enterprise focus it's coding right and then and but here's cloud co-work and and here is like well they apparently they're running instagram ads for claudia on you know for people right and so like it's kind of like this the disruption thing of uh you know opening has been doing consumer been doing just pursuing general intelligence in every modality and here is a topic that only focus on this thing but now they're sort of undercutting and doing the whole innovators dilemma thing on like everything else it's very interesting yeah but there's a very open So for me, there's like, do you know that meme where there's like the guy in the path and there's like a path this way, there's a path this way.

29:20Which way, Western man? Yeah, yeah, yeah. And for me, like the entire industry kind of like hinges on like two potential futures. So in one potential future, the market is infinitely large. There's perverse economies of scale because as soon as you put a model out there, like it kind of sublimates and all the other models catch up. and like it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows and then there's another path which is like well maybe these models actually generalize really well and all you have to do is train them with three times more money that's all you have to do and it'll just consume everything beyond it and if that's the case like you end up with basically an oligopoly for everything like you know because they're perfectly general and like so this would be like the the agi path would be like these are perfectly general they can do everything and this one is like this is actually normal software the universe is complicated you've got and nobody knows the answer my belief is if you actually look at the numbers of these companies so generally if you look at the numbers of these companies if you look at like the amount they're making and how much they they spent training the last model their gross margin positive you're like oh that's really working but if you look at like the current training that they're doing for the next model their gross margin negative so part of me thinks that a lot of them are kind of borrowing against the future and that's going to have to slow down that's going to catch up to them at some point in time but we don't really know yeah does that make sense like i mean it could be the case that the only reason this is working is because they can raise that next round and they can train that next model because these models have such a short life and so at some point in time like you know they won't be able to raise that next round for the next model and then things will kind of converge and fragment again but right now it's not totally i think the other by the way just um a meta point i think the other lesson from the last three years is and we talk about this all the time because we're on this twitter x bubble um but you know if you go back to let's say march 2024 that period it felt like a i think an open source model with an f like a you know benchmark leading capability was sort of launching on a daily basis at that point and um and so that you know that's one period suddenly it's sort of like open source takes over the world there's going to be a plethora it's not an oligopoly you know if you fast you know if you if you rewind time even before that gpt4 was number one for nine months ten months it's a long time right um and of course now we're in this era where it feels like an oligopoly um maybe some very steady state shifts and and you know it could look like this in the future too but it just it's so hard to call and i think the thing that keeps you know us up at night in a good way and bad way is that the capability progress is actually not slowing down and so until that happens right like you don't know what's going to look like but i would say for sure it's not converged like for sure like the systemic capital flows have not converged meaning right now it's still borrowing against the future to subsidize growth currently which you can do that for a period of time but but you know at the end at some point the market will rationalize that and just nobody knows what that will look like yeah or or like the drop in price of compute will will save them who knows yeah yeah i think the models need to asymptote to specific tasks you know it's like okay now opus 4.5 might be a gi at some specific task and now you can like depreciate the model over a longer time i think now not right now there's like no old model no but let me just change that mental that's that used to be my mental model let me just change it a little bit if you can raise three times if you can raise more than the accurate of anybody that uses your models that doesn't even matter It doesn't even matter.

33:00See what I'm saying? So I have an API business. My API business is 60 % margin or 70 % margin or 80 % margin. It's a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm AGI or not. And I will know if they're using it because they're using it. And unlike in the past where engineering stops me from doing that, this is very straightforward. You just train. So I also thought it was kind of like you must ask some AGI, general, general, general. But I think there's also just a possibility that the capital markets will just give them the ammunition to just go after everybody on top of them.

33:36I do wonder, though, to your point, if there's a certain task that getting marginally better isn't actually that much better. Like we've asymptoted to, you know, we can call it AGI or whatever. Actually, Ali Goadzi talks about this. Like we're already at AGI for a lot of functions in the enterprise. um that's probably for those tasks you probably could build very specific companies that focus on just getting as much value out of that task that isn't coming from the model itself there's probably a rich enterprise business to be built there i mean could be wrong on that but there's a lot of interesting examples so right if you're looking about the legal profession or whatnot and maybe that's not a great one because the models are getting better on that front too but just something where it's a bit saturated, then the value comes from services.

34:18It comes from implementation, right? It comes from all these things that actually make it useful to the end customer. One more thing I think is under-discussed in all of this is like, to what extent every task is AGI complete? I code every day. It's so fun. That's a core question, yeah. And like, when I'm talking to these models, it's not just code. I mean, it's everything, right? like i you know like it's it's health care it's legal but it's everything it's exactly that like i mean yeah that's everything like i'm asking these models to yeah to understand compliance i'm asking these models to go search the web i'm asking these models to talk about things i know in the history like that's having a full conversation with me while i i engineer and so it could be the case that like the most a you know agi complete like i'm not an agi guy like i think that's you know but like the most agi complete model will always win independent of the task and we don't know the answer to that one either yeah but it seems to me that like listen codex in my experience is for sure better than opus 4.5 for coding like it finds the hardest bugs that i work in with like it's you know the smartest developers i don't work on it it's great um but i think opus 4.5 is actually very it's got a great bedside manner and it really it really matters if you're building something very complex because like it really you know like you're you're a partner and a brainstorming partner for somebody.

35:38And I think we don't discuss enough how every task kind of has that quality. And what does that mean to like capital investment and like frontier models and submodels? Like what happened to all the special coding models? Like none of them worked, right? So do some of them. They didn't even get released. There's a whole host. We saw a bunch of them and like there's this whole theory that like there could be a... And I think one of the conclusions is like there's no such thing as a coding model. Like that's not a thing. Like you're talking into another human being and it's good at coding but like it's got to be good at everything minor disagree only because I'm pretty like have pretty high confidence that basically OpenAI will always release a GPT-5 and a GPT-5 codex like that's the coding one yeah yeah yeah the way I call it is one for Riz and one for Tiz and then like someone internal at OpenAI was like yeah that's a good way to frame it that's so funny but maybe it maybe collapses down to Riz and Tiz and that's it it's not like 100 dimensions it's two dimensions exactly bitside manner versus coding that's what it is for anybody listening to this for you when you're coding or using these models for something like that actually just be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it I think the best Soto-ish model is going to remain very important no matter what the task is.